# Defined shared layers as global variables
repeator = RepeatVector(Tx)
concatenator = Concatenate(axis=-1)
densor1 = Dense(10, activation = "tanh")
densor2 = Dense(1, activation = "relu")
activator = Activation(softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook
dotor = Dot(axes = 1)

# GRADED FUNCTION: one_step_attention

def one_step_attention(a, s_prev):
    """
    Performs one step of attention: Outputs a context vector computed as a dot product of the attention weights
    "alphas" and the hidden states "a" of the Bi-LSTM.
    
    Arguments:
    a -- hidden state output of the Bi-LSTM, numpy-array of shape (m, Tx, 2*n_a)
    s_prev -- previous hidden state of the (post-attention) LSTM, numpy-array of shape (m, n_s)
    
    Returns:
    context -- context vector, input of the next (post-attetion) LSTM cell
    """
    
    ### START CODE HERE ###
    # Use repeator to repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states "a" (≈ 1 line)
    s_prev = repeator(s_prev)
    # Use concatenator to concatenate a and s_prev on the last axis (≈ 1 line)
    concat = concatenator([a, s_prev])
    # Use densor1 to propagate concat through a small fully-connected neural network to compute the "intermediate energies" variable e. (≈1 lines)
    e = densor1(concat)
    # Use densor2 to propagate e through a small fully-connected neural network to compute the "energies" variable energies. (≈1 lines)
    energies = densor2(e)
    # Use "activator" on "energies" to compute the attention weights "alphas" (≈ 1 line)
    alphas = activator(energies)
    # Use dotor together with "alphas" and "a" to compute the context vector to be given to the next (post-attention) LSTM-cell (≈ 1 line)
    context = dotor([alphas, a])
    ### END CODE HERE ###
    
    return context

n_a = 32
n_s = 64
post_activation_LSTM_cell = LSTM(n_s, return_state = True)
output_layer = Dense(len(machine_vocab), activation=softmax)

# GRADED FUNCTION: model

def model(Tx, Ty, n_a, n_s, human_vocab_size, machine_vocab_size):
    """
    Arguments:
    Tx -- length of the input sequence
    Ty -- length of the output sequence
    n_a -- hidden state size of the Bi-LSTM
    n_s -- hidden state size of the post-attention LSTM
    human_vocab_size -- size of the python dictionary "human_vocab"
    machine_vocab_size -- size of the python dictionary "machine_vocab"

    Returns:
    model -- Keras model instance
    """
    
    # Define the inputs of your model with a shape (Tx,)
    # Define s0 and c0, initial hidden state for the decoder LSTM of shape (n_s,)
    X = Input(shape=(Tx, human_vocab_size))
    s0 = Input(shape=(n_s,), name='s0')
    c0 = Input(shape=(n_s,), name='c0')
    s = s0
    c = c0
    
    # Initialize empty list of outputs
    outputs = []
    
    ### START CODE HERE ###
    
    # Step 1: Define your pre-attention Bi-LSTM. Remember to use return_sequences=True. (≈ 1 line)
    a = Bidirectional(LSTM(n_a, return_sequences=True))(X)
    
    # Step 2: Iterate for Ty steps
    for t in range(Ty):
    
        # Step 2.A: Perform one step of the attention mechanism to get back the context vector at step t (≈ 1 line)
        context = one_step_attention(a ,s)
        
        # Step 2.B: Apply the post-attention LSTM cell to the "context" vector.
        # Don't forget to pass: initial_state = [hidden state, cell state] (≈ 1 line)
        s, _, c = post_activation_LSTM_cell(context, initial_state=[s, c])
        
        # Step 2.C: Apply Dense layer to the hidden state output of the post-attention LSTM (≈ 1 line)
        out = output_layer(s)
        
        # Step 2.D: Append "out" to the "outputs" list (≈ 1 line)
        outputs.append(out)
    
    # Step 3: Create model instance taking three inputs and returning the list of outputs. (≈ 1 line)
    model = Model(inputs=[X,s0,c0], outputs=outputs)
    
    ### END CODE HERE ###
    
    return model

def get_random_time_segment(segment_ms):
    """
    Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.
    
    Arguments:
    segment_ms -- the duration of the audio clip in ms ("ms" stands for "milliseconds")
    
    Returns:
    segment_time -- a tuple of (segment_start, segment_end) in ms
    """
    
    segment_start = np.random.randint(low=0, high=10000-segment_ms)   # Make sure segment doesn't run past the 10sec background 
    segment_end = segment_start + segment_ms - 1
    
    return (segment_start, segment_end)

# GRADED FUNCTION: is_overlapping

def is_overlapping(segment_time, previous_segments):
    """
    Checks if the time of a segment overlaps with the times of existing segments.
    
    Arguments:
    segment_time -- a tuple of (segment_start, segment_end) for the new segment
    previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments
    
    Returns:
    True if the time segment overlaps with any of the existing segments, False otherwise
    """
    
    segment_start, segment_end = segment_time
    
    ### START CODE HERE ### (≈ 4 line)
    # Step 1: Initialize overlap as a "False" flag. (≈ 1 line)
    overlap = False
    
    # Step 2: loop over the previous_segments start and end times.
    # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)
    for previous_start, previous_end in previous_segments:
        if segment_start <= previous_end and segment_end >= previous_start:
            overlap = True
    ### END CODE HERE ###

    return overlap

# GRADED FUNCTION: insert_audio_clip

def insert_audio_clip(background, audio_clip, previous_segments):
    """
    Insert a new audio segment over the background noise at a random time step, ensuring that the 
    audio segment does not overlap with existing segments.
    
    Arguments:
    background -- a 10 second background audio recording.  
    audio_clip -- the audio clip to be inserted/overlaid. 
    previous_segments -- times where audio segments have already been placed
    
    Returns:
    new_background -- the updated background audio
    """
    
    # Get the duration of the audio clip in ms
    segment_ms = len(audio_clip)
    
    ### START CODE HERE ### 
    # Step 1: Use one of the helper functions to pick a random time segment onto which to insert 
    # the new audio clip. (≈ 1 line)
    segment_time = get_random_time_segment(segment_ms)
    
    # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep 
    # picking new segment_time at random until it doesn't overlap. (≈ 2 lines)
    while is_overlapping(segment_time,previous_segments):
        segment_time = get_random_time_segment(segment_ms)

    # Step 3: Add the new segment_time to the list of previous_segments (≈ 1 line)
    previous_segments.append(segment_time)
    ### END CODE HERE ###
    
    # Step 4: Superpose audio segment and background
    new_background = background.overlay(audio_clip, position = segment_time[0])
    
    return new_background, segment_time

# GRADED FUNCTION: insert_ones

def insert_ones(y, segment_end_ms):
    """
    Update the label vector y. The labels of the 50 output steps strictly after the end of the segment 
    should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the
    50 followinf labels should be ones.
    
    
    Arguments:
    y -- numpy array of shape (1, Ty), the labels of the training example
    segment_end_ms -- the end time of the segment in ms
    
    Returns:
    y -- updated labels
    """
    
    # duration of the background (in terms of spectrogram time-steps)
    segment_end_y = int(segment_end_ms * Ty / 10000.0)
    
    # Add 1 to the correct index in the background label (y)
    ### START CODE HERE ### (≈ 3 lines)
    for i in range(segment_end_y+1, segment_end_y+51):
        if i < Ty:
            y[0, i] = 1
    ### END CODE HERE ###
    
    return y

# GRADED FUNCTION: create_training_example

def create_training_example(background, activates, negatives):
    """
    Creates a training example with a given background, activates, and negatives.
    
    Arguments:
    background -- a 10 second background audio recording
    activates -- a list of audio segments of the word "activate"
    negatives -- a list of audio segments of random words that are not "activate"
    
    Returns:
    x -- the spectrogram of the training example
    y -- the label at each time step of the spectrogram
    """
    
    # Set the random seed
    np.random.seed(18)
    
    # Make background quieter
    background = background - 20

    ### START CODE HERE ###
    # Step 1: Initialize y (label vector) of zeros (≈ 1 line)
    y = np.zeros((1, Ty))

    # Step 2: Initialize segment times as empty list (≈ 1 line)
    previous_segments = []
    ### END CODE HERE ###
    
    # Select 0-4 random "activate" audio clips from the entire list of "activates" recordings
    number_of_activates = np.random.randint(0, 5)
    random_indices = np.random.randint(len(activates), size=number_of_activates)
    random_activates = [activates[i] for i in random_indices]
    
    ### START CODE HERE ### (≈ 3 lines)
    # Step 3: Loop over randomly selected "activate" clips and insert in background
    for random_activate in random_activates:
        # Insert the audio clip on the background
        background, segment_time = insert_audio_clip(background, random_activate, previous_segments)
        # Retrieve segment_start and segment_end from segment_time
        segment_start, segment_end = segment_time
        # Insert labels in "y"
        y = insert_ones(y, segment_end)
    ### END CODE HERE ###

    # Select 0-2 random negatives audio recordings from the entire list of "negatives" recordings
    number_of_negatives = np.random.randint(0, 3)
    random_indices = np.random.randint(len(negatives), size=number_of_negatives)
    random_negatives = [negatives[i] for i in random_indices]

    ### START CODE HERE ### (≈ 2 lines)
    # Step 4: Loop over randomly selected negative clips and insert in background
    for random_negative in random_negatives:
        # Insert the audio clip on the background 
        background, _ = insert_audio_clip(background, random_negative, previous_segments)
    ### END CODE HERE ###
    
    # Standardize the volume of the audio clip 
    background = match_target_amplitude(background, -20.0)

    # Export new training example 
    file_handle = background.export("train" + ".wav", format="wav")
    print("File (train.wav) was saved in your directory.")
    
    # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)
    x = graph_spectrogram("train.wav")
    
    return x, y

# GRADED FUNCTION: model

def model(input_shape):
    """
    Function creating the model's graph in Keras.
    
    Argument:
    input_shape -- shape of the model's input data (using Keras conventions)

    Returns:
    model -- Keras model instance
    """
    
    X_input = Input(shape = input_shape)
    
    ### START CODE HERE ###
    
    # Step 1: CONV layer (≈4 lines)
    X = Conv1D(filters=196,kernel_size=15,strides=4)(X_input)                                 # CONV1D
    X = BatchNormalization()(X)                                 # Batch normalization
    X = Activation('relu')(X)                                 # ReLu activation
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)

    # Step 2: First GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences = True)(X)                                 # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                  # dropout (use 0.8)
    X = BatchNormalization()(X)                                 # Batch normalization
    
    # Step 3: Second GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences = True)(X)                                 # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                  # dropout (use 0.8)
    X = BatchNormalization()(X)                                 # Batch normalization
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)
    
    # Step 4: Time-distributed dense layer (≈1 line)
    X = TimeDistributed(Dense(1, activation = "sigmoid"))(X) # time distributed  (sigmoid)

    ### END CODE HERE ###

    model = Model(inputs = X_input, outputs = X)
    
    return model

model = load_model('./models/tr_model.h5')
opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=["accuracy"])
model.fit(X, Y, batch_size = 5, epochs=1)